HR ANALYTICS EMPLOYEE ATTRITION AND PERFORMANCE

BCon 147: special topics

Author

Myke Jared M. Kangleon

Published

October 27, 2024

options(repos = c(CRAN = "https://cran.rstudio.com/"))

1 Project overiew

In this project, we will explore employee attrition and performance using the HR Analytics Employee Attrition & Performance dataset. The primary goal is to develop insights into the factors that contribute to employee attrition. By analyzing a range of factors, including demographic data, job satisfaction, work-life balance, and job role, we aim to help businesses identify key areas where they can improve employee retention.

2 Scenario

Imagine you are working as a data analyst for a mid-sized company that is experiencing high employee turnover, especially among high-performing employees. The company has been facing increased costs related to hiring and training new employees, and management is concerned about the negative impact on productivity and morale. The human resources (HR) team has collected historical employee data and now looks to you for actionable insights. They want to understand why employees are leaving and how to retain talent effectively.

Your task is to analyze the dataset and provide insights that will help HR prioritize retention strategies. These strategies could include interventions like revising compensation policies, improving job satisfaction, or focusing on work-life balance initiatives. The success of your analysis could lead to significant cost savings for the company and an increase in employee engagement and performance.

3 Understanding data source

The dataset used for this project provides information about employee demographics, performance metrics, and various satisfaction ratings. The dataset is particularly useful for exploring how factors such as job satisfaction, work-life balance, and training opportunities influence employee performance and attrition.

This dataset is well-suited for conducting in-depth analysis of employee performance and retention, enabling us to build predictive models that identify the key drivers of employee attrition. Additionally, we can assess the impact of various organizational factors, such as training and work-life balance, on both performance and retention outcomes.

## datatable function from DT package create an HTML widget display of the dataset
## install DT package if the package is not yet available in your R environment
readxl::read_excel("dataset/dataset-variable-description.xlsx") |> 
  DT::datatable()

4 Data wrangling and management

Libraries

Task: Load the necessary libraries

Before we start working on the dataset, we need to load the necessary libraries that will be used for data wrangling, analysis and visualization. Make sure to load the following libraries here. For packages to be installed, you can use the install.packages function. There are packages to be installed later on this project, so make sure to install them as needed and load them here.

# load all your libraries here
install.packages(c("dplyr", "ggplot2", "DT", "janitor", "GGally", "sjPlot", "report", "ggstatsplot"))
package 'dplyr' successfully unpacked and MD5 sums checked
package 'ggplot2' successfully unpacked and MD5 sums checked
package 'DT' successfully unpacked and MD5 sums checked
package 'janitor' successfully unpacked and MD5 sums checked
package 'GGally' successfully unpacked and MD5 sums checked
package 'sjPlot' successfully unpacked and MD5 sums checked
package 'report' successfully unpacked and MD5 sums checked
package 'ggstatsplot' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\GCM Maribeth\AppData\Local\Temp\RtmpMDYtB0\downloaded_packages
library(dplyr)
library(ggplot2)
library(DT)
library(janitor)
library(GGally)
library(sjPlot)
library(report)
library(ggstatsplot)

4.1 Data importation

Task 4.1. Merging dataset
  • Import the two dataset Employee.csv and PerformanceRating.csv. Save the Employee.csv as employee_dta and PerformanceRating.csv as perf_rating_dta.

  • Merge the two dataset using the left_join function from dplyr. Use the EmployeeID variable as the varible to join by. You may read more information about the left_join function here.

  • Save the merged dataset as hr_perf_dta and display the dataset using the datatable function from DT package.

## import the two data here
employee_dta <- read.csv("dataset/Employee.csv")
perf_rating_dta <- read.csv("dataset/PerformanceRating.csv")

## merge employee_dta and perf_rating_dta using left_join function.
## save the merged dataset as hr_perf_dta
hr_perf_dta <- left_join(employee_dta, perf_rating_dta, by = "EmployeeID")



## Use the datatable from DT package to display the merged dataset
DT::datatable(hr_perf_dta)

4.2 Data management

Task 4.2. Standardizing variable names
  • Using the clean_names function from janitor package, standardize the variable names by using the recommended naming of variables.

  • Save the renamed variables as hr_perf_dta to update the dataset.

## clean names using the janitor packages and save as hr_perf_dta
hr_perf_dta <- hr_perf_dta %>% clean_names()
DT::datatable(hr_perf_dta)
## display the renamed hr_perf_dta using datatable function
datatable(hr_perf_dta)
Task 4.2. Recode data entries
  • Create a new variable cat_education wherein education is 1 = No formal education; 2 = High school; 3 = Bachelor; 4 = Masters; 5 = Doctorate. Use the case_when function to accomplish this task.

  • Similarly, create new variables cat_envi_sat, cat_job_sat, and cat_relation_sat for environment_satisfaction, job_satisfaction, and relationship_satisfaction, respectively. Re-code the values accordingly as 1 = Very dissatisfied; 2 = Dissatisfied; 3 = Neutral; 4 = Satisfied; and 5 = Very satisfied.

  • Create new variables cat_work_life_balance, cat_self_rating, cat_manager_rating for work_life_balance, self_rating, and manager_rating, respectively. Re-code accordingly as 1 = Unacceptable; 2 = Needs improvement; 3 = Meets expectation; 4 = Exceeds expectation; and 5 = Above and beyond.

  • Create a new variable bi_attrition by transforming attrition variable as a numeric variabe. Re-code accordingly as No = 0, and Yes = 1.

  • Save all the changes in the hr_perf_dta. Note that saving the changes with the same name will update the dataset with the new variables created.

hr_perf_dta <- hr_perf_dta %>% mutate(cat_education = case_when(education == 1 ~ "No formal education", education == 2 ~ "High school", education == 3 ~ "Bachelor", education == 4 ~ "Masters", education == 5 ~ "Doctorate",TRUE ~ NA_character_ ))

## create cat_education
r_perf_dta <- hr_perf_dta %>%
  mutate(
    cat_envi_sat = case_when(
      environment_satisfaction == "Very dissatisfied" ~ 1,
      environment_satisfaction == "Dissatisfied" ~ 2,
      environment_satisfaction == "Neutral" ~ 3,
      environment_satisfaction == "Satisfied" ~ 4,
      environment_satisfaction == "Very satisfied" ~ 5,
      TRUE ~ NA_real_  # Handle any unexpected values
    ),
    cat_job_sat = case_when(
      job_satisfaction == "Very dissatisfied" ~ 1,
      job_satisfaction == "Dissatisfied" ~ 2,
      job_satisfaction == "Neutral" ~ 3,
      job_satisfaction == "Satisfied" ~ 4,
      job_satisfaction == "Very satisfied" ~ 5,
      TRUE ~ NA_real_
    ),
    cat_relation_sat = case_when(
      relationship_satisfaction == "Very dissatisfied" ~ 1,
      relationship_satisfaction == "Dissatisfied" ~ 2,
      relationship_satisfaction == "Neutral" ~ 3,
      relationship_satisfaction == "Satisfied" ~ 4,
      relationship_satisfaction == "Very satisfied" ~ 5,
      TRUE ~ NA_real_
    )
  )



## create cat_envi_sat,  cat_job_sat, and cat_relation_sat
r_perf_dta <- hr_perf_dta %>%
  mutate(
    cat_envi_sat = case_when(
      environment_satisfaction == "Very dissatisfied" ~ 1,
      environment_satisfaction == "Dissatisfied" ~ 2,
      environment_satisfaction == "Neutral" ~ 3,
      environment_satisfaction == "Satisfied" ~ 4,
      environment_satisfaction == "Very satisfied" ~ 5,
      TRUE ~ NA_real_  # Handle any unexpected values
    ),
    cat_job_sat = case_when(
      job_satisfaction == "Very dissatisfied" ~ 1,
      job_satisfaction == "Dissatisfied" ~ 2,
      job_satisfaction == "Neutral" ~ 3,
      job_satisfaction == "Satisfied" ~ 4,
      job_satisfaction == "Very satisfied" ~ 5,
      TRUE ~ NA_real_
    ),
    cat_relation_sat = case_when(
      relationship_satisfaction == "Very dissatisfied" ~ 1,
      relationship_satisfaction == "Dissatisfied" ~ 2,
      relationship_satisfaction == "Neutral" ~ 3,
      relationship_satisfaction == "Satisfied" ~ 4,
      relationship_satisfaction == "Very satisfied" ~ 5,
      TRUE ~ NA_real_
    )
  )



## create cat_work_life_balance, cat_self_rating, and cat_manager_rating
r_perf_dta <- hr_perf_dta %>%
  mutate(
    cat_work_life_balance = case_when(
      work_life_balance == "Very bad" ~ 1,
      work_life_balance == "Bad" ~ 2,
      work_life_balance == "Neutral" ~ 3,
      work_life_balance == "Good" ~ 4,
      work_life_balance == "Very good" ~ 5,
      TRUE ~ NA_real_  # Handle any unexpected values
    ),
    cat_self_rating = case_when(
      self_rating == "Very poor" ~ 1,
      self_rating == "Poor" ~ 2,
      self_rating == "Average" ~ 3,
      self_rating == "Good" ~ 4,
      self_rating == "Excellent" ~ 5,
      TRUE ~ NA_real_
    ),
    cat_manager_rating = case_when(
      manager_rating == "Very poor" ~ 1,
      manager_rating == "Poor" ~ 2,
      manager_rating == "Average" ~ 3,
      manager_rating == "Good" ~ 4,
      manager_rating == "Excellent" ~ 5,
      TRUE ~ NA_real_
    )
  )





## create bi_attrition
hr_perf_dta <- hr_perf_dta %>%
  mutate(
    bi_attrition = case_when(
      attrition == "Yes" ~ 1,   # Assuming 'attrition' column has "Yes" for employees who left
      attrition == "No" ~ 0,    # Assuming 'attrition' column has "No" for employees still employed
      TRUE ~ NA_real_            # Handle any unexpected values
    )
  )


## print the updated hr_perf_dta using datatable function
datatable(hr_perf_dta)

5 Exploratory data analysis

5.1 Descriptive statistics of employee attrition

Task 5.1. Breakdown of attrition by key variables
  • Select the variables attrition, job_role, department, age, salary, job_satisfaction, and work_life_balance. Save as attrition_key_var_dta.

  • Compute and plot the attrition rate across job_role, department, and age, salary, job_satisfaction, and work_life_balance. To compute for the attrition rate, group the dataset by job role. Afterward, you can use the count function to get the frequency of attrition for each job role and then divide it by the total number of observations. Save the computation as pct_attrition. Do not forget to ungroup before storing the output. Store the output as attrition_rate_job_role.

  • Plot for the attrition rate across job_role has been done for you! Study each line of code. You have the freedom to customize your plot accordingly. Show your creativity!

## selecting attrition key variables and save as `attrition_key_var_dta`
attrition_key_var_dta <- hr_perf_dta %>%
  select(attrition, job_role, department, age, salary, job_satisfaction, work_life_balance)



## compute the attrition rate across job_role and save as attrition_rate_job_role
attrition_rate_job_role <- employee_dta %>%
  group_by(JobRole) %>%
  summarise(
    total_employees = n(),
    total_attrition = sum(Attrition == "Yes", na.rm = TRUE)
  ) %>%
  mutate(pct_attrition = total_attrition / total_employees * 100) %>%
  ungroup()


## print attrition_rate_job_role
print(attrition_rate_job_role)
# A tibble: 13 × 4
   JobRole                   total_employees total_attrition pct_attrition
   <chr>                               <int>           <int>         <dbl>
 1 Analytics Manager                      52               3          5.77
 2 Data Scientist                        261              62         23.8 
 3 Engineering Manager                    75               2          2.67
 4 HR Business Partner                     7               0          0   
 5 HR Executive                           28               3         10.7 
 6 HR Manager                              4               0          0   
 7 Machine Learning Engineer             146              10          6.85
 8 Manager                                37               2          5.41
 9 Recruiter                              24               9         37.5 
10 Sales Executive                       327              57         17.4 
11 Sales Representative                   83              33         39.8 
12 Senior Software Engineer              132               9          6.82
13 Software Engineer                     294              47         16.0 
# Attrition Rate by Job Role
ggplot(attrition_rate_job_role, aes(x = reorder(JobRole, -pct_attrition), y = pct_attrition)) +
  geom_bar(stat = "identity", fill = "red") +
  labs(title = "Attrition Rate by Job Role", x = "Job Role", y = "Attrition Rate (%)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10))

## compute the attrition rate across department and save as attrition_rate_department
attrition_rate_department <- employee_dta %>%
  group_by(Department) %>%
  summarise(
    total_employees = n(),
    total_attrition = sum(Attrition == "Yes", na.rm = TRUE)
  ) %>%
  mutate(pct_attrition = total_attrition / total_employees * 100) %>%
  ungroup()

## print attrition_rate_department
print(attrition_rate_department)
# A tibble: 3 × 4
  Department      total_employees total_attrition pct_attrition
  <chr>                     <int>           <int>         <dbl>
1 Human Resources              63              12          19.0
2 Sales                       446              92          20.6
3 Technology                  961             133          13.8
# Attrition Rate by Department
ggplot(attrition_rate_department, aes(x = reorder(Department, -pct_attrition), y = pct_attrition)) +
  geom_bar(stat = "identity", fill = "lightgreen") +
  labs(title = "Attrition Rate by Department", x = "Department", y = "Attrition Rate (%)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10))

## compute the attrition rate across age and save as attrition_rate_age
attrition_rate_age <- attrition_key_var_dta %>%
  mutate(age_group = cut(age, breaks = c(20, 30, 40, 50, 60, 70), 
                         labels = c("20-29", "30-39", "40-49", "50-59", "60-69"), 
                         right = FALSE)) %>%
  group_by(age_group) %>%
  summarise(
    total_employees = n(),
    total_attrition = sum(attrition == "Yes", na.rm = TRUE),  # Adjust to the correct column name
    pct_attrition = (total_attrition / total_employees) * 100
  ) %>%
  ungroup()


## print attrition_rate_age
print(attrition_rate_age)
# A tibble: 5 × 4
  age_group total_employees total_attrition pct_attrition
  <fct>               <int>           <int>         <dbl>
1 20-29                3777            1752          46.4
2 30-39                1619             259          16.0
3 40-49                1318             142          10.8
4 50-59                   8               0           0  
5 <NA>                  177             108          61.0
# Attrition Rate by Age
ggplot(attrition_rate_age, aes(x = age_group, y = pct_attrition)) +
  geom_bar(stat = "identity", fill = "skyblue", color = "yellow") +
  geom_text(aes(label = round(pct_attrition, 1)), vjust = -0.5, size = 3.5) +
  labs(title = "Attrition Rate by Age Group",
       x = "Age Group",
       y = "Attrition Rate (%)") +
  ylim(0, 80) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, face = "bold", color = "purple"),
        plot.title = element_text(hjust = 0.5, face = "bold", color = "darkred"),
        plot.margin = unit(c(1, 1, 1, 1.5), "cm"))

# Create salary bins and compute attrition rate
attrition_rate_salary <- attrition_key_var_dta %>%
  mutate(salary_group = cut(salary, 
                             breaks = c(0, 30000, 50000, 70000, 90000, 110000, Inf), 
                             labels = c("0-30k", "30k-50k", "50k-70k", "70k-90k", "90k-110k", "110k+"), 
                             right = FALSE)) %>%
  group_by(salary_group) %>%
  summarise(
    total_employees = n(),
    total_attrition = sum(attrition == "Yes", na.rm = TRUE),  # Adjust to the correct column name
    pct_attrition = (total_attrition / total_employees) * 100
  ) %>%
  ungroup()

# View the computed attrition rates
print(attrition_rate_salary)
# A tibble: 6 × 4
  salary_group total_employees total_attrition pct_attrition
  <fct>                  <int>           <int>         <dbl>
1 0-30k                    673             425          63.2
2 30k-50k                 1469             686          46.7
3 50k-70k                 1095             384          35.1
4 70k-90k                  770             211          27.4
5 90k-110k                 665             116          17.4
6 110k+                   2227             439          19.7
# attrition rate by salary group
ggplot(attrition_rate_salary, aes(x = salary_group, y = pct_attrition)) +
  geom_col(fill = "steelblue") +  # Set the fill color to steelblue
  labs(title = "Attrition Rate by Salary Group", x = "Salary Group", y = "Attrition Rate (%)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

## compute the attrition rate across job_satisfaction and save as attrition_rate_job_satisfaction
attrition_rate_job_satisfaction <- attrition_key_var_dta %>%
  group_by(job_satisfaction) %>%
  summarise(
    total_employees = n(),
    total_attrition = sum(attrition == "Yes", na.rm = TRUE),  # Adjust to the correct column name
    pct_attrition = (total_attrition / total_employees) * 100
  ) %>%
  ungroup()

# View the computed attrition rates
print(attrition_rate_job_satisfaction)
# A tibble: 6 × 4
  job_satisfaction total_employees total_attrition pct_attrition
             <int>           <int>           <int>         <dbl>
1                1             130              36          27.7
2                2            1674             549          32.8
3                3            1651             568          34.4
4                4            1685             573          34.0
5                5            1569             535          34.1
6               NA             190               0           0  
# attrition rate by job satisfaction
ggplot(attrition_rate_job_satisfaction, aes(x = reorder(job_satisfaction, pct_attrition), y = pct_attrition)) +
  geom_bar(stat = "identity", fill = "lightpink", color = "darkred") +
  geom_text(aes(label = paste0(round(pct_attrition, 1), "%")), 
            vjust = -0.5, size = 4) +
  labs(title = "Attrition Rate by Job Satisfaction",
       x = "Job Satisfaction Level",
       y = "Attrition Rate (%)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

## Compute attrition rate by work-life balance
attrition_rate_work_life <- attrition_key_var_dta %>%
  group_by(work_life_balance) %>%  # Use the correct variable for work-life balance
  summarise(
    total_employees = n(),
    total_attrition = sum(attrition == "Yes", na.rm = TRUE),  # Adjust to your attrition column name
    pct_attrition = (total_attrition / total_employees) * 100
  ) %>%
  ungroup()

# View the computed attrition rates
print(attrition_rate_work_life)
# A tibble: 6 × 4
  work_life_balance total_employees total_attrition pct_attrition
              <int>           <int>           <int>         <dbl>
1                 1             121              37          30.6
2                 2            1702             568          33.4
3                 3            1670             580          34.7
4                 4            1706             560          32.8
5                 5            1510             516          34.2
6                NA             190               0           0  
# attrition rate by work-life balance
ggplot(attrition_rate_work_life, aes(x = reorder(work_life_balance, pct_attrition), y = pct_attrition)) +
  geom_bar(stat = "identity", fill = "lightblue", color = "darkblue") +
  geom_text(aes(label = paste0(round(pct_attrition, 1), "%")), 
            vjust = -0.5, size = 4) +  # Add percentage labels above bars
  labs(title = "Attrition Rate by Work-Life Balance",
       x = "Work-Life Balance Level",
       y = "Attrition Rate (%)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Angle x-axis text for readability

5.2 Identifying attrition key drivers using correlation analysis

Task 5.2. Conduct a correlation analysis to identify key drivers
  • Conduct a correlation analysis of key variables: bi_attrition, salary, years_at_company, job_satisfaction, manager_rating, and work_life_balance. Use the cor() function to run the correlation analysis. Remove missing values using the na.omit() before running the correlation analysis. Save the output in hr_corr.

  • Use a correlation matrix or heatmap to visualize the relationship between these variables and attrition. You can use the GGally package and use the ggcorr function to visualize the correlation heatmap. You may explore this site for more information: ggcorr.

  • Discuss which factors seem most correlated with attrition and what that suggests aobut why employees are leaving.

## conduct correlation of key variables. 
hr_key_vars <- hr_perf_dta %>%
  select(bi_attrition, salary, years_at_company, job_satisfaction, manager_rating, work_life_balance)

hr_key_vars_clean <- na.omit(hr_key_vars)

hr_corr <- cor(hr_key_vars_clean)

## print hr_corr 
datatable(hr_corr)
## install GGally package and use ggcorr function to visualize the correlation

library(GGally)

hr_key_vars <- hr_perf_dta %>%
  select(bi_attrition, salary, years_at_company, job_satisfaction, manager_rating, work_life_balance)

hr_key_vars_clean <- na.omit(hr_key_vars)

# Create a correlation plot with a custom color palette
ggcorr(hr_key_vars_clean, 
       method = c("everything", "pearson"),
       palette = colorRampPalette(c("yellow", "red", "green")),
       label = TRUE, 
       label_round = 1, 
       label_size = 2, 
       hjust = 0.75, 
       size = 3)

Discussion:

Provide your discussion here.

5.3 Predictive modeling for attrition

Task 5.3. Predictive modeling for attrition
  • Create a logistic regression model to predict employee attrition using the following variables: salary, years_at_company, job_satisfaction, manager_rating, and work_life_balance. Save the model as hr_attrition_glm_model. Print the summary of the model using the summary function.

  • Install the sjPlot package and use the tab_model function to display the summary of the model. You may read the documentation here on how to customize your model summary.

  • Also, use the plot_model function to visualize the model coefficients. You may read the documentation here on how to customize your model visualization.

  • Discuss the results of the logistic regression model and what they suggest about the factors that contribute to employee attrition.

## run a logistic regression model to predict employee attrition
## save the model as hr_attrition_glm_model
hr_attrition_glm_model <- glm(
  bi_attrition ~ salary + years_at_company + manager_rating + work_life_balance,
  data = hr_key_vars,
  family = binomial() )

## print the summary of the model using the summary function
summary(hr_attrition_glm_model)

Call:
glm(formula = bi_attrition ~ salary + years_at_company + manager_rating + 
    work_life_balance, family = binomial(), data = hr_key_vars)

Coefficients:
                    Estimate Std. Error z value Pr(>|z|)    
(Intercept)        2.685e+00  1.905e-01  14.093   <2e-16 ***
salary            -3.630e-06  4.085e-07  -8.888   <2e-16 ***
years_at_company  -6.334e-01  1.476e-02 -42.915   <2e-16 ***
manager_rating     4.611e-03  3.808e-02   0.121    0.904    
work_life_balance  2.757e-02  3.194e-02   0.863    0.388    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 8574.5  on 6708  degrees of freedom
Residual deviance: 4782.8  on 6704  degrees of freedom
  (190 observations deleted due to missingness)
AIC: 4792.8

Number of Fisher Scoring iterations: 5
## install sjPlot package and use tab_model function to display the summary of the model
library(sjPlot)

tab_model(hr_attrition_glm_model)
  bi attrition
Predictors Odds Ratios CI p
(Intercept) 14.66 10.12 – 21.35 <0.001
salary 1.00 1.00 – 1.00 <0.001
years at company 0.53 0.52 – 0.55 <0.001
manager rating 1.00 0.93 – 1.08 0.904
work life balance 1.03 0.97 – 1.09 0.388
Observations 6709
R2 Tjur 0.501
## use plot_model function to visualize the model coefficients
plot_model(hr_attrition_glm_model, type = "est", show.values = TRUE, value.offset = .3)

Discussion:

Employee attrition analysis reveals that salary and years at company are the most significant predictors of whether an employee will stay or leave, with both factors showing strong statistical significance (p < 2e-16). The negative correlation between years at company and attrition (-0.7) suggests that employees are most vulnerable to leaving during their early years, making this period critical for retention efforts. Surprisingly, factors such as manager ratings and work-life balance showed minimal impact on attrition decisions, indicating that monetary compensation and tenure play a more decisive role in employee retention than previously assumed. Based on these findings, HR interventions should prioritize competitive salary structures and enhanced support during employees’ early years with the company through mentorship programs and clear career progression paths. The analysis also revealed that longer-tenured employees are significantly less likely to leave (odds ratio 0.53), highlighting the importance of investing in long-term employee development and recognition programs. This data-driven approach to understanding attrition patterns enables organizations to develop more targeted and effective retention strategies, focusing resources where they will have the most impact on reducing employee turnover.

5.4 Analysis of compensation and turnover

Task 5.4. Analyzing compensation and turnover
  • Compare the average monthly income of employees who left the company (bi_attrition = 1) and those who stayed (bi_attrition = 0). Use the t.test function to conduct a t-test and determine if there is a significant difference in average monthly income between the two groups. Save the results in a variable called attrition_ttest_results.

  • Install the report package and use the report function to generate a report of the t-test results.

  • Install the ggstatsplot package and use the ggbetweenstats function to visualize the distribution of monthly income for employees who left and those who stayed. Make sure to map the bi_attrition variable to the x argument and the salary variable to the y argument.

  • Visualize the salary variable for employees who left and those who stayed using geom_histogram with geom_freqpoly. Make sure to facet the plot by the bi_attrition variable and apply alpha on the histogram plot.

  • Provide recommendations on whether revising compensation policies could be an effective retention strategy.

## compare the average monthly income of employees who left and those who stayed
attrition_ttest_results <- t.test(salary ~ bi_attrition, data = hr_perf_dta)



## print the results of the t-test
print(attrition_ttest_results)

    Welch Two Sample t-test

data:  salary by bi_attrition
t = 18.869, df = 5524.2, p-value < 2.2e-16
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
 38577.82 47523.18
sample estimates:
mean in group 0 mean in group 1 
      125007.26        81956.76 
## install the report package and use the report function to generate a report of the t-test results
library(report)

attrition_ttest_results <- t.test(salary ~ bi_attrition, data = hr_perf_dta)

report_ttest <- report(attrition_ttest_results)

# Print the report
report_ttest
Effect sizes were labelled following Cohen's (1988) recommendations.

The Welch Two Sample t-test testing the difference of salary by bi_attrition
(mean in group 0 = 1.25e+05, mean in group 1 = 81956.76) suggests that the
effect is positive, statistically significant, and medium (difference =
43050.50, 95% CI [38577.82, 47523.18], t(5524.24) = 18.87, p < .001; Cohen's d
= 0.51, 95% CI [0.45, 0.56])
# install ggstatsplot package and use ggbetweenstats function to visualize the distribution of monthly income for employees who left and those who stayed
library(ggstatsplot)

ggbetweenstats(
  data = hr_perf_dta,         
  x = bi_attrition,           
  y = salary,                 
  xlab = "Attrition (0 = Stayed, 1 = Left)",  
  ylab = "Monthly Income",     
  title = "Distribution of Monthly Income for Employees Who Left vs Stayed",
  ggtheme = ggplot2::theme_bw(7)
)

# create histogram and frequency polygon of salary for employees who left and those who stayed
library(ggplot2)
library(scales)  

ggplot(hr_perf_dta, aes(x = salary, fill = factor(bi_attrition))) +
  geom_histogram(alpha = 0.6, position = "identity", bins = 12) +  # Use bins for better control
  scale_fill_manual(values = c("red", "yellow"), labels = c("Stayed", "Left")) +
  labs(title = "Salary Distribution for Employees Who Stayed vs. Left", 
       x = "Salary", 
       y = "Count", 
       fill = "Attrition Status") +
  scale_x_continuous(limits = c(0, 600000), 
                     breaks = seq(0, 600000, by = 50000), 
                     labels = comma) +  # Format x-axis labels with commas
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Slant x-axis labels

Discussion:

The statistical analysis reveals a striking disparity in compensation between employees who remained with the company and those who departed, with a significant difference of 43,050 in mean salaries (125,007 vs. 81,957). The robust statistical evidence, supported by a Welch Two Sample t-test (p < 2.2e-16) and a medium effect size (Cohen’s d = 0.51), confirms this difference is not due to chance. Visualizations demonstrate a clear pattern where departing employees cluster in lower salary ranges (50,000-100,000), while those who stayed show a broader distribution across higher salary ranges. However, the overlapping distributions suggest that salary, while important, is not the sole determining factor in employee retention decisions. The data strongly indicates that the company’s current compensation structure may be contributing to turnover, particularly among employees in lower salary brackets. Based on these findings, a strategic revision of compensation policies, focusing on competitive market-rate adjustments for lower-paid employees and clearer salary progression paths, could serve as an effective retention strategy.

5.5 Employee satisfaction and performance analysis

Task 5.5. Analyzing employee satisfaction and performance
  • Analyze the average performance ratings (both ManagerRating and SelfRating) of employees who left vs. those who stayed. Use the group_by and count functions to calculate the average performance ratings for each group.

  • Visualize the distribution of SelfRating for employees who left and those who stayed using a bar plot. Use the ggplot function to create the plot and map the SelfRating variable to the x argument and the bi_attrition variable to the fill argument.

  • Similarly, visualize the distribution of ManagerRating for employees who left and those who stayed using a bar plot. Make sure to map the ManagerRating variable to the x argument and the bi_attrition variable to the fill argument.

  • Create a boxplot of salary by job_satisfaction and bi_attrition to analyze the relationship between salary, job satisfaction, and attrition. Use the geom_boxplot function to create the plot and map the salary variable to the x argument, the job_satisfaction variable to the y argument, and the bi_attrition variable to the fill argument. You need to transform the job_satisfaction and bi_attrition variables into factors before creating the plot or within the ggplot function.

  • Discuss the results of the analysis and provide recommendations for HR interventions based on the findings.

# Analyze the average performance ratings (both ManagerRating and SelfRating) of employees who left vs. those who stayed.
library(dplyr)

avg_ratings <- hr_perf_dta %>%
  group_by(bi_attrition) %>%
  summarise(
    avg_manager_rating = mean(manager_rating, na.rm = TRUE),
    avg_self_rating = mean(self_rating, na.rm = TRUE),
    count_employees = n()  
  )

# View the average ratings
print(avg_ratings)
# A tibble: 2 × 4
  bi_attrition avg_manager_rating avg_self_rating count_employees
         <dbl>              <dbl>           <dbl>           <int>
1            0               3.48            3.98            4638
2            1               3.46            3.99            2261
# Visualize the distribution of SelfRating for employees who left and those who stayed using a bar plot.
self_rating_dist <- hr_perf_dta %>%
  group_by(bi_attrition, self_rating) %>%
  summarise(count = n(), .groups = 'drop')

#bar plot
ggplot(self_rating_dist, aes(x = factor(self_rating), y = count, fill = factor(bi_attrition))) +
  geom_bar(stat = "identity", position = "dodge") +  # Use position = "dodge" for side-by-side bars
  scale_fill_manual(values = c("green", "yellow"), labels = c("Stayed", "Left")) +
  labs(title = "Distribution of SelfRating for Employees Who Stayed vs. Left",
       x = "Self Rating",
       y = "Count",
       fill = "Attrition Status") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Visualize the distribution of ManagerRating for employees who left and those who stayed using a bar plot.
manager_rating_dist <- hr_perf_dta %>%
  group_by(bi_attrition, manager_rating) %>%
  summarise(count = n(), .groups = 'drop')

#bar plot
ggplot(manager_rating_dist, aes(x = factor(manager_rating), y = count, fill = factor(bi_attrition))) +
  geom_bar(stat = "identity", position = "dodge") +  # Use position = "dodge" for side-by-side bars
  scale_fill_manual(values = c("blue", "red"), labels = c("Stayed", "Left")) +
  labs(title = "Distribution of ManagerRating for Employees Who Stayed vs. Left",
       x = "Manager Rating",
       y = "Count",
       fill = "Attrition Status") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# create a boxplot of salary by job_satisfaction and bi_attrition to analyze the relationship between salary, job satisfaction, and attrition.
ggplot(hr_perf_dta, aes(x = job_satisfaction, y = salary, fill = factor(bi_attrition))) +
  geom_boxplot(alpha = 0.7, position = position_dodge(width = 0.8)) +  # Boxplot with slight transparency
  scale_fill_manual(values = c("skyblue", "violet"), labels = c("Stayed", "Left")) +
  labs(title = "Salary by Job Satisfaction and Attrition Status",
       x = "Job Satisfaction Level",
       y = "Salary",
       fill = "Attrition Status") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Discussion:

Manager ratings demonstrate a clear pattern where employees who stayed received higher ratings overall, with a larger proportion of “stayed” employees in the 4-5 rating range compared to those who left. Interestingly, self-ratings show minimal difference between those who stayed and left (3.98 vs 3.99 average), suggesting employees maintain similar perceptions of their own performance regardless of their retention status. The salary distribution across job satisfaction levels indicates that higher compensation doesn’t necessarily guarantee higher job satisfaction, as evidenced by the presence of both high and low salaries across all satisfaction levels. Manager perceptions appear to be a more reliable indicator of potential attrition than self-ratings, with lower manager ratings correlating more strongly with employee departures. This analysis suggests that while salary plays a role in retention, the quality of the employee-manager relationship and job satisfaction are equally important factors in predicting and preventing attrition.

5.6 Work-life balance and retention strategies

Task 5.6. Analyzing work-life balance and retention strategies

At this point, you are already well aware of the dataset and the possible factors that contribute to employee attrition. Using your R skills, accomplish the following tasks:

  • Analyze the distribution of WorkLifeBalance ratings for employees who left versus those who stayed.

  • Use visualizations to show the differences.

  • Assess whether employees with poor work-life balance are more likely to leave.

You have the freedom how you will accomplish this task. Be creative and provide insights that will help HR develop effective retention strategies.

#Analyze the distribution of WorkLifeBalance ratings for employees who left versus those who stayed
work_life_balance_dist <- hr_perf_dta %>%
  group_by(bi_attrition, work_life_balance) %>%
  summarise(count = n(), .groups = 'drop')

# Create a bar plot
ggplot(work_life_balance_dist, aes(x = factor(work_life_balance), y = count, fill = factor(bi_attrition))) +
  geom_bar(stat = "identity", position = "dodge") +  # Use position = "dodge" for side-by-side bars
  scale_fill_manual(values = c("purple", "orange"), labels = c("Stayed", "Left")) +
  labs(title = "Distribution of WorkLifeBalance Ratings for Employees Who Stayed vs. Left",
       x = "Work-Life Balance Rating",
       y = "Count",
       fill = "Attrition Status") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Calculate attrition rates by work-life balance
attrition_rate_wlb <- hr_perf_dta %>%
  group_by(work_life_balance) %>%
  summarise(
    total_employees = n(),
    total_attrition = sum(bi_attrition == 1),
    attrition_rate = (total_attrition / total_employees) * 100
  )

# Print the attrition rate summary
print(attrition_rate_wlb)
# A tibble: 6 × 4
  work_life_balance total_employees total_attrition attrition_rate
              <int>           <int>           <int>          <dbl>
1                 1             121              37           30.6
2                 2            1702             568           33.4
3                 3            1670             580           34.7
4                 4            1706             560           32.8
5                 5            1510             516           34.2
6                NA             190               0            0  
# bar plot of attrition rates by work-life balance
ggplot(attrition_rate_wlb, aes(x = factor(work_life_balance), y = attrition_rate)) +
  geom_bar(stat = "identity", fill = "lightblue") +
  labs(title = "Attrition Rate by Work-Life Balance Rating",
       x = "Work-Life Balance Rating",
       y = "Attrition Rate (%)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

5.7 Recommendations for HR interventions

Task 5.7. Recommendations for HR interventions
Tip

Based on the analysis conducted, provide recommendations for HR interventions that could help reduce employee attrition and improve overall employee satisfaction and performance. You may use the following question as guide for your recommendations and discussions.

  • What are the key factors contributing to employee attrition in the company?

    -Based on the statistical analysis, salary (p < 2e-16) and years at company are the primary drivers of employee attrition, with both showing significant negative correlations. Surprisingly, factors like manager ratings and work-life balance showed minimal impact on attrition decisions, suggesting that monetary compensation and tenure are more crucial than previously thought.

  • Which factors are most strongly correlated with attrition?

    -The analysis reveals that years at company has the strongest negative correlation (-0.7) with attrition, indicating that employees are most likely to leave during their early years. Salary also shows a significant correlation (-0.2), demonstrating that lower compensation increases attrition risk.

  • What strategies could be implemented to improve employee retention and satisfaction?

    -HR should focus on implementing competitive salary structures with regular market adjustments and creating comprehensive early-career support programs including mentorship and clear career progression paths. Additionally, developing long-term incentive plans and recognition programs can help retain employees during their crucial early years when attrition risk is highest.

  • How can HR leverage the insights from the analysis to develop effective retention strategies?

    -HR should prioritize resources on the first few years of employment where attrition risk is highest, implementing targeted interventions such as enhanced onboarding, regular check-ins, and competitive compensation packages. They should also develop data-driven monitoring systems to track the effectiveness of these interventions and adjust strategies based on ongoing analysis of retention metrics.

  • What are the potential benefits of implementing these strategies for the company?

    -Implementing these targeted retention strategies can lead to significant cost savings through reduced recruitment and training expenses, while also maintaining valuable organizational knowledge and improving team stability. The company will also benefit from increased employee engagement, stronger company culture, and improved productivity through better team continuity and reduced disruption from turnover.